摘要
由于水质数据特征复杂、关联度参差不齐而导致溶解氧浓度预测难度较大,为提高水质溶解氧浓度预测的准确性,提出了一种基于特征工程和北方苍鹰优化算法的长短期记忆网络(Feature Engineering-Northern Goshawk Optimization-Long Short Term Memory,FE-NGO-LSTM)混合模型。首先对水质数据集进行缺失值补齐、特征筛选与特征多项式构造,然后基于NGO-LSTM模型优化模型参数,提升预测性能;对不同多项式阶数下的特征预测效果进行分析之后,将该模型与基于灰狼优化算法、鲸鱼优化算法及粒子群优化算法的LSTM模型进行对比;最后,在太湖流域东苕溪城南监测断面对该模型进行了验证,计算FE-NGO-LSTM模型预见期为4,8,12,16,20,24 h的预测结果。试验结果显示:当多项式阶数为2阶时,模型预测效果最好,FE-NGO-LSTM模型相比基于其他优化算法的LSTM模型,平均绝对误差、均方误差、均方根误差分别至少降低9.0%,12.9%及6.3%,且随着预见期的增加,预测误差仍在可接受范围内,说明FE-NGO-LSTM模型在预测溶解氧浓度时具有一定优势与泛化性。
Due to complex characteristics and uneven correlation of water quality data,it is difficult to predict dissolved oxygen concentration.To improve the prediction accuracy of water quality dissolved oxygen concentration,a Feature Engineering and Northern Goshawk Optimization-Long Short Term Memory(FE-NGO-LSTM)hybrid model was proposed.Firstly,missing value imputation,feature screening,and feature polynomial construction were performed on the water quality dataset.Then,the model parameters were optimized based on the NGO-LSTM model to improve prediction performance.After analyzing the feature prediction performance under different polynomial orders,the model was compared with LSTM models based on grey wolf optimization algorithm,whale optimization algorithm,and particle swarm optimization algorithm.Finally,the model was validated with the dataset of the Chengnan monitoring section on east Tiaoxi River,and the prediction results of the FE-NGO-LSTM model were calculated for prediction periods of 4,8,12,16,20,and 24 hours.The experimental results demonstrated that when the polynomial order was 2nd,the model had the best prediction performance.Compared with LSTM models based on other optimization algorithms,the average absolute error,mean square error,and root mean square error of FE-NGO-LSTM model were reduced at least 9.0%,12.9%,and 6.3%respectively.Moreover,as the prediction period increased,the prediction error was still within an acceptable range,indicating that the FE-NGO-LSTM model has certain advantages and generalization in predicting dissolved oxygen concentration.
作者
虞佳颖
肖姚
YU Jiaying;XIAO Yao(School of Hydraulic Engineering,Zhejiang Tongji Vocational College of Science and Technology,Hangzhou 311231,China;College of Aerospace Engineering,Chongqing University,Chongqing 400044,China)
出处
《人民长江》
北大核心
2024年第10期86-93,共8页
Yangtze River
基金
国家自然科学基金项目(51408505)
浙江省水利厅科技项目(RC1954)
浙江省高校基本科研业务费项目(FRF20QN003)。
关键词
水质预测
溶解氧
特征工程
深度学习
北方苍鹰优化算法
耦合模型
苕溪流域
太湖流域
water quality prediction
dissolved oxygen
feature engineering
deep learning
Northern Goshawk
optimization algorithm
coupling model
Tiaoxi River Basin
Taihu Lake Basin
